DQN layers when state space and action space are multi dimensional
This is not necessarily the only way to do this but it would be the approach I'd take.
Assuming your agents position is a vector in $\mathbb{R}^d$, then I would have the network take as input this position vector and pass it through a fully connected layer. I would also take as input the matrix and pass it through a convolutional layer(s) and flatten the output so it is now also a vector in $\mathbb{R}^{d'}$. I would then concatenate these together so you have a vector in $\mathbb{R}^{d + d'}$ and pass this through some fully connected layers as usual.
As for how many layers and which activation to use this is something you'll have to do by trial and error as this really is problem specific.
Your output would typically be a score for each of the action combinations, though as one of your outputs is a matrix this could potentially make things expensive to compute as you would need $2^{n \times m}$ binary representations just for the matrix ($n$ is the number of rows and $m$ is the number of columns).